Article
Computer Science, Artificial Intelligence
Meenal Jain, Gagandeep Kaur, Vikas Saxena
Summary: This paper introduces the field of data stream mining and its application in anomaly detection in network traffic. Due to concept drift in the data streams, traditional machine learning algorithms face challenges in accuracy and false alarms. To address this issue, the paper proposes two new techniques for concept drift detection and utilizes sliding window and K-Means Clustering for data reduction and training dataset enhancement. Experimental results demonstrate improved classification accuracy and performance metrics using the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Carlo Baldassi
Summary: We introduce an evolutionary algorithm called recombinator-k-means for optimizing the highly nonconvex kmeans problem. Its defining feature is that its crossover step involves all the members of the current generation, stochastically recombining them with a repurposed variant of the k-means++ seeding algorithm. The recombination also uses a reweighting mechanism that realizes a progressively sharper stochastic selection policy and ensures that the population eventually coalesces into a single solution. We compare this scheme with a state-of-the-art alternative, a more standard genetic algorithm with deterministic pairwise-nearest-neighbor crossover and an elitist selection policy, of which we also provide an augmented and efficient implementation. Extensive tests on large and challenging datasets (both synthetic and real word) show that for fixed population sizes recombinator-k-means is generally superior in terms of the optimization objective, at the cost of a more expensive crossover step. When adjusting the population sizes of the two algorithms to match their running times, we find that for short times the (augmented) pairwise-nearest-neighbor method is always superior, while at longer times recombinator-k-means will match it and, on the most difficult examples, take over. We conclude that the reweighted whole-population recombination is more costly but generally better at escaping local minima Moreover, it is algorithmically simpler and more general (it could be applied even to k-medians or k-medoids, for example).
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Cheng Chen, Yen-Liang Chen, Jyun-Yun Lu
Summary: K-Means algorithm is one of the most famous and popular clustering algorithms in the world, known for its simple structure, easy implementation, high efficiency, and fast convergence speed. This article introduces an improvement to past variants of K-Means used in evolutionary clustering, considering both past and future clustering results, and extending K-Means to multiple cycles, resulting in more consistent, stable, and smooth clustering results.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Automation & Control Systems
Uri Stemmer
Summary: This research presents a new algorithm operating in the local model of differential privacy for solving the Euclidean k-means problem, significantly reducing additive error while maintaining multiplicative error. The study shows that the obtained additive error in handling the k-means objective is almost optimal in terms of its dependency on the database size.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Materials Science, Coatings & Films
Hao Chen, Yuanyuan Lu, Kanghui Wu, Xinyun Wang, Dejian Liu
Summary: The TiC/Fe ceramic metal composite with WC particles addition was successfully produced by laser deposition process. The addition of WC improved the microstructure and mechanical properties of the Fe-based MMC. The formation of a core-rim structure enhanced the interface strength and toughness of the MMC.
SURFACE & COATINGS TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Ahmed Fahim
Summary: The k-means method divides N objects into k clusters based on mean values, with linear time complexity and dependence on knowing the number of clusters and initial centers. This research introduces a method able to detect near-optimal values for k and initial centers without prior knowledge, resulting in improved final result quality. The proposed method combines DBSCAN and k-means to converge to global minima and has a time complexity of o(n log n).
JOURNAL OF COMPUTATIONAL SCIENCE
(2021)
Article
Computer Science, Information Systems
Jing Liu, Fuyuan Cao, Jiye Liang
Summary: In this paper, a centroids-guided deep multi-view k-means clustering method is proposed, which incorporates deep representation learning into the multi-view k-means objective. The method produces more k-means-friendly representations by reducing the loss between each representation and its assigned cluster centroid.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongfu Liu, Junxiang Chen, Jennifer Dy, Yun Fu
Summary: K-means is a widely used clustering algorithm known for its simplicity and efficiency. This review paper focuses on generalizing K-means to solve challenging and complex problems. It unifies the available approaches in terms of data representation, distance measure, label assignment, and centroid updating. Concrete applications of modified K-means formulations are reviewed, including iterative subspace projection and clustering, consensus clustering, constrained clustering, domain adaptation, and outlier detection.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Avgoustinos Vouros, Stephen Langdell, Mike Croucher, Eleni Vasilaki
Summary: K-Means is a widely used algorithm for data clustering, but it has limitations such as only finding local minima and being sensitive to initial centroid positions. Various K-Means variations and initialization techniques have been proposed, with more sophisticated techniques reducing the need for complex clustering methods. Deterministic methods generally outperform stochastic methods, but there is a trade-off where simpler stochastic methods run multiple times can result in better clustering.
Article
Computer Science, Artificial Intelligence
Luc Giffon, Valentin Emiya, Hachem Kadri, Liva Ralaivola
Summary: K-means algorithm and Lloyd's algorithm have expanded beyond their original clustering purposes to play pivotal roles in various machine learning and data analysis techniques. QuicK-means is an efficient extension of K-means that reduces computational complexity through sparse matrix products, demonstrating benefits through experimental results.
Article
Computer Science, Artificial Intelligence
Peter Olukanmi, Fulufhelo Nelwamondo, Tshilidzi Marwala
Summary: A key drawback of k-means algorithm is its susceptibility to local minima. The authors propose a technique for comparing initializations directly and selecting the best one based on the maximum minimum inter-center distance. The experiments and mathematical analysis show significant efficiency gains and improved accuracy compared to repeated k-means.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Marco Capo, Aritz Perez, Jose A. Antonio
Summary: The K-means algorithm is a popular clustering method, but its performance depends heavily on the initialization phase. Researchers have developed various initialization techniques to address this issue. This article introduces a cost-effective Split-Merge step that can restart the K-means algorithm after reaching a fixed point, reducing error and computing fewer distances.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Miaomiao Li, Yi Zhang, Suyuan Liu, Zhe Liu, Xinzhong Zhu
Summary: Multiple kernel clustering (MKC) aims to determine the optimal kernel from several pre-computed basic kernels. A new algorithm called simple multiple kernel k-means with kernel weight regularization (SMKKM-KWR) is proposed to overcome the issue of sparse or over-selected kernel weight coefficients. Experimental results show that SMKKM-KWR achieves effective and efficient clustering performance.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Simon Harris, Renato Cordeiro De Amorim
Summary: This paper compares the performance of 17 different algorithms on 6,000 synthetic and 28 real-world data sets to investigate the sensitivity of k-means to its initial centroids. The results show that different algorithms may excel in different clustering scenarios, providing valuable insights for those considering k-means for complex clustering tasks.
Article
Computer Science, Interdisciplinary Applications
Rasim M. Alguliyev, Ramiz M. Aliguliyev, Lyudmila Sukhostat
Summary: This article introduces a new parallel batch clustering algorithm based on the k-means algorithm, which reduces computation complexity by splitting the dataset into multiple partitions and proposes a method to determine the optimal batch size. Experimental results show the practical applicability of this method for handling Big Data.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Chemistry, Physical
Iman Sengupta, Suddhapalli S. S. Sharat Kumar, Surjya K. Pal, Sudipto Chakraborty
Summary: In the process of preparing graphite oxide, pretreatment, KMnO4, and sulfuric acid have significant impacts on the chemical oxidation reaction. KMnO4 mainly oxidizes carbon atoms by withdrawing electrons, while sulfuric acid and water molecules penetrate the interlayers to form functionalities.
FULLERENES NANOTUBES AND CARBON NANOSTRUCTURES
(2022)
Article
Automation & Control Systems
Ritam Upadhyay, Abhishek Asi, Pravanjan Nayak, Nidhi Prasad, Debasish Mishra, Surjya K. Pal
Summary: Artificial intelligence is revolutionizing the manufacturing industry by introducing flexible robots that collaborate with humans to enhance productivity. This article presents an AI-based solution for real-time grasping of cuboid- and cylindrical-shaped objects without prior knowledge of their 3D structure, achieving high accuracy.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Review
Engineering, Mechanical
Akash Mukhopadhyay, Probir Saha
Summary: In recent years, additive friction stir deposition (AFS-D) has become a promising technique for fabricating near-net-shaped metal 3D structures. However, there are deviations from the ideal microstructural evolution and mechanical performances, which are affected by process metrics such as process parameters and tool design. Therefore, it is important to study the correlation between process metrics, microstructure, and mechanical performance in AFS-D.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2022)
Article
Thermodynamics
Omkar Mypati, Tariq Anwaar, Desham Mitra, Surjya Kanta Pal, Prakash Srirangam
Summary: This study describes the sustainability of busbars in lithium-ion batteries and investigates the changes in process parameters of friction stir welded (FSW) samples at different electrical conductivity levels. The results show that the electrical conductivity varies with the formation of intermetallic compounds and changes in grain size at the Al-Cu joint.
APPLIED THERMAL ENGINEERING
(2023)
Article
Materials Science, Multidisciplinary
Mayank Verma, Probir Saha
Summary: The present study used a triple-spiral micro-grooves featured tool to mitigate the improper material inter-mixing during micro-friction stir welding. The material intermixing and its flow were assessed by analyzing process responses and weld characteristics. The highest micro-groove depth (0.12 mm) resulted in improved tensile properties with a higher mean joint efficiency of 77.18%.
MATERIALS CHARACTERIZATION
(2023)
Article
Materials Science, Multidisciplinary
Matruprasad Rout, Surjya K. Pal, Shiv Brat Singh
Summary: In this study, the microstructure evolution of austenitic stainless steel after deformation at elevated temperatures was investigated through thermo-mechanical processing. The results showed that at temperatures of 900 degrees C and 1000 degrees C, the microstructures of samples held for 2 seconds consisted of deformed grains, while the sample held at 1100 degrees C showed nearly complete recrystallization. The increase in holding time resulted in a decrease in low angle boundaries and an increase in high angle grain boundaries at all three temperatures.
MATERIALS CHEMISTRY AND PHYSICS
(2023)
Article
Automation & Control Systems
Riya Tapwal, Pallav Kumar Deb, Sudip Misra, Surjya Kanta Pal
Summary: Storing data from IIoT sensors in blockchain for monitoring applications can cause management issues like bloating. This study focuses on generating traces using an ARIMA model and storing only metadata, resulting in reduced delay and managed data. Size of traces for storage on the store is determined by considering principal parameters and S&G blocks are generated accordingly. Feasibility of S&G is demonstrated with errors and regret in the range of 0.07-0.10 and 0.20-0.25, respectively, using the appropriate ARIMA model.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Md Shamim Shah, Deepa Gupta, Probir Saha, Ashwani Assam, Chiranjit Sarkar
Summary: This paper aims to develop an efficient debris-flushing technique for micro-EDM dressing by providing rotation to the dielectric medium, resulting in significant improvement in process stability. A setup was developed to impart rotation to the dielectric medium, and the efficiency of this flushing approach was evaluated by monitoring and analyzing debris expulsion in real time. The results demonstrated the capability of the proposed flushing approach to improve process stability and achieve better debris evacuation compared to the no rotation condition.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Materials Science, Multidisciplinary
Rishabh Swarnkar, Souvik Karmakar, Surjya K. Pal
Summary: This article proposes a novel friction stir backward extrusion (FSBE) process for the fabrication of bimetallic tubular components. The process enables uniform cladding and void-free bonding between different sets of materials through diffusion and frictional heat. Intermetallic compounds (IMCs) were confirmed at the interface through energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) analyses. The strong metallurgical bonding between the substrate and cladding materials was verified through a flattening test. This process opens up new opportunities for applications in electrical, structural, lightweight, and corrosion-resistant fields.
MATERIALS TODAY COMMUNICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Omkar Mypati, Avishek Mukherjee, Debasish Mishra, Surjya Kanta Pal, Partha Pratim Chakrabarti, Arpan Pal
Summary: This article provides a detailed survey of AI algorithms and their applications in manufacturing, including casting, forming, machining, welding, additive manufacturing, and supply chain management. It discusses the evolution of processes, automation using signal and image processing, and the application of ML and AI algorithms. The article also reviews the development of robotics and cloud-based technologies and highlights the benefits of AI in manufacturing. It concludes by discussing manufacturing use cases and the need for cognitive skills in manufacturing for future research.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Engineering, Chemical
Soumya Sangita Nayak, Md Perwej Iqbal, Rahul Jain, Surjya K. Pal, Prakash Srirangam
Summary: This study aims to model temperature distribution in friction stir welding (FSW) using various backing plates and polygonal pin profiles. The experimental results show the importance of temperature on the grain size and tensile strength of the materials. However, determining the temperature at each point of the weld is difficult and expensive in experiments. Therefore, simulations are performed to accomplish the objective. The 3-D transient multiphysics model developed for FSW combines multiple physical phenomena and is validated by experiments. The model is Industry 4.0-compliant and can predict weld quality.
JOURNAL OF ADHESION SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Manufacturing
Debolina Sen, Surjya K. Pal, Sushanta K. Panda
Summary: A novel strategy involving a concave shoulder tool with three different concavities (3°, 6°, and 9°) and double pass was devised to successfully fabricate longitudinal friction stir welded tubes. Increasing concavity resulted in the formation of flash and thinning of the weld zone due to excessive heat generation caused by enhanced tool contact with tube curvature. The tool with a 3° concavity resulted in negligible pores and excellent weld zone strength, making it suitable for welding tubes using friction stir welding.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE
(2023)
Article
Engineering, Manufacturing
Debolina Sen, Bhupesh Singh Katiyar, Sushanta Kumar Panda, Surjya Kanta Pal
Summary: The effect of weld zone on the formability of FSWed AA 5083-O tubes during end expansion was investigated through experiments and finite element simulations. The incorporation of BW fracture model in the FE model predicted the non-linear deformation path and fracture of the weld zone, which was in agreement with experimental results. Microscopic examination revealed void occurrence due to matrix debonding and subsequent fracture of the weld zone. It was concluded that the BW fracture model can aid in tool design and prediction of failure limits during end expansion of welded tubes.
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Rahul Ajmeria, Mayukh Mondal, Reya Banerjee, Tamesh Halder, Pallav Kumar Deb, Debasish Mishra, Pravanjan Nayak, Sudip Misra, Surjya Kanta Pal, Debashish Chakravarty
Summary: The Industrial Internet of Things (IIoT) and its applications have undergone changes with the introduction of artificial intelligence and machine learning. To overcome the limitations of data-centric techniques, the Brain-Computer Interface (BCI) is proposed as a solution that incorporates human intuition. This study presents a comprehensive examination of the feasibility of utilizing BCI techniques, particularly Electroencephalography (EEG), in industrial applications. The study includes an extensive literature review on EEG basics, signal processing techniques, paradigms, and application scope. Potential use cases, pros and cons, challenges, and solutions are identified. Lab-scale experiments using a single-channel EEG headset demonstrate how this minimalistic setup can enable complex applications in manufacturing processes with an overall accuracy of 70%.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2023)
Article
Computer Science, Hardware & Architecture
Riya Tapwal, Pallav Kumar Deb, Sudip Misra, Surjya Kanta Pal
Summary: In this work, the authors propose Shadows, a virtual blockchain for achieving parallel consensus and efficient data management in industries. By virtualizing the nodes of the blockchain network and creating different blockchains for various activities, Shadows achieves resource-efficient real-time consensus. Through experiments, Shadows is shown to efficiently utilize resources, achieve real-time consensus, provide better data access, and balance the system load using smart contracts.
IEEE TRANSACTIONS ON COMPUTERS
(2023)